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The need for efficient computational screening of molecular candidates thatpossess desired properties frequently arises in various scientific andengineering problems, including drug discovery and materials design. However,the large size of the search space containing the candidates and thesubstantial computational cost of high-fidelity property prediction modelsmakes screening practically challenging. In this work, we propose a generalframework for constructing and optimizing a virtual screening (HTVS) pipelinethat consists of multi-fidelity models. The central idea is to optimallyallocate the computational resources to models with varying costs and accuracyto optimize the return-on-computational-investment (ROCI). Based on bothsimulated as well as real data, we demonstrate that the proposed optimal HTVSframework can significantly accelerate screening virtually without anydegradation in terms of accuracy. Furthermore, it enables an adaptiveoperational strategy for HTVS, where one can trade accuracy for efficiency.

Optimal Decision Making in High-Throughput Virtual Screening Pipelines